from torch import nn
import math
import torch.utils.model_zoo as model_zoo
import torch
import torch.nn.functional as F
from copy import deepcopy
from other_layers import *
from functools import partial
from other_layers import IncepText

model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}


def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)  # 1/16
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)  # 1/32
        self.avgpool = nn.AdaptiveAvgPool2d(output_size=1)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x, ret_fea=False):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)

        x = self.layer3(x)

        x = self.layer4(x)
        if ret_fea:
            return x
        x = self.avgpool(x)

        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

def resnet18(pretrained=False, **kwargs):
    """Constructs a ResNet-18 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
    return model


def resnet34(pretrained=False, **kwargs):
    """Constructs a ResNet-34 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
    return model


def resnet50(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
    return model


def resnet101(pretrained=False, **kwargs):
    """Constructs a ResNet-101 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
    return model


def resnet152(pretrained=False, **kwargs):
    """Constructs a ResNet-152 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
    return model



def resnet50_cat( num_classes, pretrained=True):
    base_model = resnet50(pretrained=pretrained)
    base_model.fc = cat_linear(2048, num_classes)
    return base_model

def resnet101_cat( num_classes, pretrained=True):
    base_model = resnet101(pretrained=pretrained)
    base_model.fc = cat_linear(2048, num_classes)
    return base_model

def resnet50_cat_dilate( num_classes, pretrained=True):
    base_model = resnet50(pretrained=pretrained)
    base_model.fc = cat_linear(2048, num_classes)
    base_model.layer3.apply(partial(_nostride_dilate, dilate=2))
    base_model.layer4.apply(partial(_nostride_dilate, dilate=4))
    return base_model

def resnet101_cat_dilate( num_classes, pretrained=True):
    base_model = resnet101(pretrained=pretrained)
    base_model.fc = cat_linear(2048, num_classes)
    base_model.layer3.apply(partial(_nostride_dilate, dilate=2))
    base_model.layer4.apply(partial(_nostride_dilate, dilate=4))
    return base_model

def resnet101_cat_dilate2( num_classes, pretrained=True):
    base_model = resnet101(pretrained=pretrained)
    base_model.fc = cat_linear(2048, num_classes)
    base_model.layer2.apply(partial(_nostride_dilate, dilate=2))
    base_model.layer3.apply(partial(_nostride_dilate, dilate=4))
    base_model.layer4.apply(partial(_nostride_dilate, dilate=8))
    return base_model


class Resnet50_merge34(nn.Module):
    '''
    0.9818
    '''

    def __init__(self, num_classes, pretrained=True):
        super(Resnet50_merge34, self).__init__()
        base_model = resnet50(pretrained=pretrained)
        self.inplanes = 64
        self.conv1 = base_model.conv1
        self.bn1 = base_model.bn1
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = base_model.layer1
        self.layer2 = base_model.layer2
        self.layer3 = base_model.layer3  # 1/16
        self.layer4 = base_model.layer4  # 1/32

        self.avgpool = nn.AdaptiveAvgPool2d(output_size=1)

        self.fc = cat_linear(in_features=2048 + 1024, out_features_list=num_classes)

        self.layer3.apply(partial(_nostride_dilate, dilate=2))
        self.layer4.apply(partial(_nostride_dilate, dilate=4))

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        l3 = self.layer3(x)
        l4 = self.layer4(l3)
        dl4 = F.upsample(l4, size=l3.size()[2:], mode='bilinear')
        x = torch.cat([dl4, l3], dim=1)

        x = self.avgpool(x)

        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x


class Resnet101_merge34(nn.Module):

    def __init__(self, num_classes, pretrained=True):
        super(Resnet101_merge34, self).__init__()
        base_model = resnet101(pretrained=pretrained)
        self.inplanes = 64
        self.conv1 = base_model.conv1
        self.bn1 = base_model.bn1
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = base_model.layer1
        self.layer2 = base_model.layer2
        self.layer3 = base_model.layer3  # 1/16
        self.layer4 = base_model.layer4  # 1/32

        self.avgpool = nn.AdaptiveAvgPool2d(output_size=1)

        self.fc = cat_linear(in_features=2048 + 1024, out_features_list=num_classes)
        self.layer3.apply(partial(_nostride_dilate, dilate=2))
        self.layer4.apply(partial(_nostride_dilate, dilate=4))

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        l3 = self.layer3(x)
        l4 = self.layer4(l3)
        x = torch.cat([l4, l3], dim=1)

        x = self.avgpool(x)

        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

class Res101_IncepText(nn.Module):
    def __init__(self, num_classes, pretrained=True):
        super(Res101_IncepText, self).__init__()
        base_model = resnet101(pretrained=pretrained)
        self.inplanes = 64
        self.conv1 = base_model.conv1
        self.bn1 = base_model.bn1
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = base_model.layer1
        self.layer2 = base_model.layer2
        self.layer3 = base_model.layer3  # 1/16
        self.layer4 = base_model.layer4  # 1/32
        self.layer4.apply(partial(_nostride_dilate, dilate=2))

        self.incept = IncepText(in_channels=2048,out_channels=1024)

        self.avgpool = nn.AdaptiveAvgPool2d(output_size=1)

        self.fc = cat_linear(in_features=1024, out_features_list=num_classes)


    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.incept(x)

        x = self.avgpool(x)

        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x


class Res101_Fuse(nn.Module):
    def __init__(self, num_classes, pretrained=True):
        super(Res101_Fuse, self).__init__()
        base_model = resnet101(pretrained=pretrained)
        self.inplanes = 64
        self.conv1 = base_model.conv1
        self.bn1 = base_model.bn1
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = base_model.layer1
        self.layer2 = base_model.layer2   #      512
        self.layer3 = base_model.layer3  # 1/16  1024
        self.layer4 = base_model.layer4  # 1/32  2048
        self.layer4.apply(partial(_nostride_dilate, dilate=2))

        self.incept = IncepText(in_channels=2048,out_channels=1024)

        self.avgpool = nn.AdaptiveAvgPool2d(output_size=1)

        self.fc = cat_linear(in_features=2048, out_features_list=num_classes)
        self.convl23 = nn.Sequential(
            nn.Conv2d(512,1024,kernel_size=1),
            nn.BatchNorm2d(1024),
            nn.ReLU(inplace=True)
        )

        self.convl42 = nn.Sequential(
            nn.Conv2d(2048, 1024, kernel_size=1),
            nn.BatchNorm2d(1024),
            nn.ReLU(inplace=True)
        )

        self.convl24 = nn.Sequential(
            nn.Conv2d(512,1024,kernel_size=1),
            nn.BatchNorm2d(1024),
            nn.ReLU(inplace=True)
        )


    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        l2 = self.layer2(x)
        l3 = self.layer3(l2)
        f23 = self.convl23(l2) + F.upsample(l3, size=l2.size()[2:], mode='bilinear')

        l4 = self.layer4(l3)
        f24 = self.convl24(l2) + F.upsample(self.convl42(l4), size=l2.size()[2:], mode='bilinear')

        x = torch.cat([f23,f24],dim=1)

        x = self.avgpool(x)

        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x


class Res101_FuseIncept(nn.Module):
    def __init__(self, num_classes, pretrained=True):
        super(Res101_FuseIncept, self).__init__()
        base_model = resnet101(pretrained=pretrained)
        self.inplanes = 64
        self.conv1 = base_model.conv1
        self.bn1 = base_model.bn1
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = base_model.layer1
        self.layer2 = base_model.layer2   #      512
        self.layer3 = base_model.layer3  # 1/16  1024
        self.layer4 = base_model.layer4  # 1/32  2048
        self.layer4.apply(partial(_nostride_dilate, dilate=2))

        self.inceptl3 = IncepText(in_channels=1024,out_channels=1024)
        self.inceptl4 = IncepText(in_channels=1024, out_channels=1024)

        self.avgpool = nn.AdaptiveAvgPool2d(output_size=1)

        self.fc = cat_linear(in_features=2048, out_features_list=num_classes)
        self.convl23 = nn.Sequential(
            nn.Conv2d(512,1024,kernel_size=1),
            nn.BatchNorm2d(1024),
            nn.ReLU(inplace=True)
        )

        self.convl42 = nn.Sequential(
            nn.Conv2d(2048, 1024, kernel_size=1),
            nn.BatchNorm2d(1024),
            nn.ReLU(inplace=True)
        )

        self.convl24 = nn.Sequential(
            nn.Conv2d(512,1024,kernel_size=1),
            nn.BatchNorm2d(1024),
            nn.ReLU(inplace=True)
        )


    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        l2 = self.layer2(x)
        l3 = self.layer3(l2)
        f23 = self.convl23(l2) + F.upsample(l3, size=l2.size()[2:], mode='bilinear')
        f23 = self.inceptl3(f23)

        l4 = self.layer4(l3)
        f24 = self.convl24(l2) + F.upsample(self.convl42(l4), size=l2.size()[2:], mode='bilinear')
        f24 = self.inceptl4(f24)


        x = torch.cat([f23,f24],dim=1)

        x = self.avgpool(x)

        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x



class Resnet101_dnod(nn.Module):

    def __init__(self, num_classes, pretrained=True):
        super(Resnet101_dnod, self).__init__()
        base_model = resnet101(pretrained=pretrained)
        self.inplanes = 64
        self.conv1 = base_model.conv1
        self.bn1 = base_model.bn1
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = base_model.layer1
        self.layer2 = base_model.layer2
        self.layer3 = deepcopy(base_model.layer3)  # 1/16
        self.layer4 = deepcopy(base_model.layer4)  # 1/32


        self.avgpool = nn.AdaptiveAvgPool2d(output_size=1)

        self.fc = cat_linear(in_features=2048, out_features_list=num_classes)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)

        # dilate mode
        self.layer3.apply(partial(_dilate_mode, dilate=2))
        self.layer4.apply(partial(_dilate_mode, dilate=4))
        l3_d = self.layer3(x)
        l4_d = self.layer4(l3_d)

        # no dilate mode
        self.layer3.apply(_nodilate_mode)
        self.layer4.apply(_nodilate_mode)

        l3_nod = self.layer3(x)
        l4_nod = self.layer4(l3_nod)

        x = self.avgpool(l4_nod) + self.avgpool(l4_d)

        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x


class Resnet101_dnod2(nn.Module):

    def __init__(self, num_classes, pretrained=True):
        super(Resnet101_dnod2, self).__init__()
        base_model = resnet101(pretrained=pretrained)
        self.inplanes = 64
        self.conv1 = base_model.conv1
        self.bn1 = base_model.bn1
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = base_model.layer1
        self.layer2 = base_model.layer2

        self.layer3 = deepcopy(base_model.layer3)  # 1/16
        self.layer4 = deepcopy(base_model.layer4)  # 1/32

        self.avgpool = nn.AdaptiveAvgPool2d(output_size=1)

        self.fc_nod = cat_linear(in_features=2048, out_features_list=num_classes)
        self.fc_d = cat_linear(in_features=2048, out_features_list=num_classes)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)

        # dilate mode
        self.layer3.apply(partial(_dilate_mode, dilate=2))
        self.layer4.apply(partial(_dilate_mode, dilate=4))
        l3_d = self.layer3(x)
        l4_d = self.layer4(l3_d)


        # no dilate mode
        self.layer3.apply(_nodilate_mode)
        self.layer4.apply(_nodilate_mode)

        l3_nod = self.layer3(x)
        l4_nod = self.layer4(l3_nod)


        l4_nod = self.avgpool(l4_nod)
        l4_nod = l4_nod.view(l4_nod.size(0), -1)
        l4_nod = self.fc_nod(l4_nod)

        l4_d = self.avgpool(l4_d)
        l4_d = l4_d.view(l4_d.size(0), -1)
        l4_d = self.fc_d(l4_d)

        return (l4_d + l4_nod)/2.


def _nostride_dilate( m, dilate):
    classname = m.__class__.__name__
    if classname=='Conv2d':
        # the convolution with stride
        if m.stride == (2, 2):
            m.stride = (1, 1)
            if m.kernel_size == (3, 3):
                m.dilation = (dilate//2, dilate//2)
                m.padding = (dilate//2, dilate//2)
        # other convoluions
        else:
            if m.kernel_size == (3, 3):
                m.dilation = (dilate, dilate)
                m.padding = (dilate, dilate)

def _nodilate_mode( m):
    classname = m.__class__.__name__
    if classname=='Conv2d':
        # the convolution with stride
        if m.stride_change:
            m.stride = (2, 2)
            m.stride_change = False
            if m.kernel_size == (3, 3) and m.dilation_change:
                m.dilation = (1, 1)
                m.padding = (1, 1)
                m.dilation_change = False
        # other convoluions
        else:
            if m.kernel_size == (3, 3) and m.dilation_change:
                m.dilation = (1, 1)
                m.padding = (1, 1)
                m.dilation_change = False


def _dilate_mode( m, dilate):
    classname = m.__class__.__name__
    if classname=='Conv2d':
        # the convolution with stride
        if m.stride == (2, 2):
            m.stride = (1, 1)
            m.stride_change = (2,1)
            if m.kernel_size == (3, 3):
                m.dilation = (dilate//2, dilate//2)
                m.padding = (dilate//2, dilate//2)
                m.dilation_change = (3,dilate//2)
        # other convoluions
        else:
            m.stride_change = False
            if m.kernel_size == (3, 3):
                m.dilation = (dilate, dilate)
                m.padding = (dilate, dilate)
                m.dilation_change = (3, dilate)

if __name__ == '__main__':
    model = resnet50_cat_dilate(num_classes=[2,3])
    print model
    # torch.save(model.state_dict(),'./testaa.pth')
    # # # #
    #
    # x = torch.FloatTensor(3,3,224,224).zero_()
    # x = torch.autograd.Variable(x)
    # # # # # import numpy as np
    # # # # #
    # # # # # attrs = torch.from_numpy(np.array([0,2,1]))
    # # # # #
    # y = model(x)
    # print y
    # y = model(x)
    # print y
    # # # #

